Taking into account the input observations \(X\)  or the prediction errors can be more proper in various applications than imposing the same decisions for all. To address this, the notions of Equal performance, Status-quo preserving, or Error parity measure whether a model is equally accurate for individuals in the sensitive and nonsensitive groups. As discussed in [1]}, it is often measured by using three common metrics: Equal sensitivity or Equal opportunity [2]}, Equal sensitivity and specificity or Equalized odds, and Equal positive predictive value or Predictive parity [3]}. In the case of a binary decision, common metrics usually compute the difference between True Positive Rate and/or False Positive Rate for majority and minority groups. Therefore, Fairness in the sense of Equal performance is reached when this difference is zero. Specifically:
An Equal opportunity metric is given by
\( |\mathbb {P}(\hat{Y}=1 | A=0, Y=1) - \mathbb {P}(\hat{Y}=1 | A=1, Y=1) |\,,\) 
